IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v274y2023ics0360544223006710.html
   My bibliography  Save this article

Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles

Author

Listed:
  • Liu, Yonggang
  • Chen, Qianyou
  • Li, Jie
  • Zhang, Yuanjian
  • Chen, Zheng
  • Lei, Zhenzhen

Abstract

Transportation accounts for a large proportion of energy consumption and environmental pollution, and eco-routing is recognized as a potential solution to green mobility. In this context, this study investigates the co-optimization problem of eco-routing on a road network for heterogeneous continuous vehicle flow. Firstly, the energy consumption estimation models for 33 types of vehicles are constructed by artificial neural networks with a large amount of historical driving data. In this case, the Bureau of Public Roads function and traffic light models are imported to establish the road network model, accurately reflecting the impact of congestion and traffic lights change on vehicle speeds. Finally, based on the energy consumption difference of different vehicles, a collaborative heterogeneous multi-vehicle eco-routing optimization strategy is proposed to improve the overall economy in the road network. Simulation experiments are conducted under different traffic flow conditions and multiple road networks. The results verify that an energy-saving improvement up to 11.50% is obtained compared with the conventional path planning approach, providing efficient promotions to the energy consumption reduction of connected and automated vehicles.

Suggested Citation

  • Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223006710
    DOI: 10.1016/j.energy.2023.127277
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223006710
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127277?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alam, Md. Saniul & McNabola, Aonghus, 2014. "A critical review and assessment of Eco-Driving policy & technology: Benefits & limitations," Transport Policy, Elsevier, vol. 35(C), pages 42-49.
    2. Murakami, Keisuke, 2017. "A new model and approach to electric and diesel-powered vehicle routing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 23-37.
    3. Scora, George & Boriboonsomsin, Kanok & Barth, Matthew, 2015. "Value of eco-friendly route choice for heavy-duty trucks," Research in Transportation Economics, Elsevier, vol. 52(C), pages 3-14.
    4. Ortega-Cabezas, Pedro-Miguel & Colmenar-Santos, Antonio & Borge-Diez, David & Blanes-Peiró, Jorge-Juan, 2021. "Can eco-routing, eco-driving and eco-charging contribute to the European Green Deal? Case Study: The City of Alcalá de Henares (Madrid, Spain)," Energy, Elsevier, vol. 228(C).
    5. Goeke, Dominik & Schneider, Michael, 2015. "Routing a mixed fleet of electric and conventional vehicles," European Journal of Operational Research, Elsevier, vol. 245(1), pages 81-99.
    6. Goeke, D. & Schneider, M., 2015. "Routing a Mixed Fleet of Electric and Conventional Vehicles," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65939, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Turkensteen, Marcel, 2017. "The accuracy of carbon emission and fuel consumption computations in green vehicle routing," European Journal of Operational Research, Elsevier, vol. 262(2), pages 647-659.
    8. Ku, Donggyun & Choi, Minje & Yoo, Nakyoung & Shin, Seungheon & Lee, Seungjae, 2021. "A new algorithm for eco-friendly path guidance focused on electric vehicles," Energy, Elsevier, vol. 233(C).
    9. Macrina, Giusy & Laporte, Gilbert & Guerriero, Francesca & Di Puglia Pugliese, Luigi, 2019. "An energy-efficient green-vehicle routing problem with mixed vehicle fleet, partial battery recharging and time windows," European Journal of Operational Research, Elsevier, vol. 276(3), pages 971-982.
    10. Sivak, Michael & Schoettle, Brandon, 2012. "Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy," Transport Policy, Elsevier, vol. 22(C), pages 96-99.
    11. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan, 2021. "Electric vehicle routing problem with machine learning for energy prediction," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 24-55.
    12. Cedric De Cauwer & Wouter Verbeke & Thierry Coosemans & Saphir Faid & Joeri Van Mierlo, 2017. "A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions," Energies, MDPI, vol. 10(5), pages 1-18, May.
    13. Huang, Yuhan & Ng, Elvin C.Y. & Zhou, John L. & Surawski, Nic C. & Chan, Edward F.C. & Hong, Guang, 2018. "Eco-driving technology for sustainable road transport: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 596-609.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qin, Yanyan & Xiao, Tengfei & Wang, Hua, 2024. "Optimization strategy for connected automated vehicles to reduce energy consumption on freeway in rainy weather," Energy, Elsevier, vol. 296(C).
    2. Jin Li & Hongping Zhang & Huasheng Liu & Shiyan Wang, 2024. "Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network," Sustainability, MDPI, vol. 16(2), pages 1-16, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Panagiotis Fafoutellis & Eleni G. Mantouka & Eleni I. Vlahogianni, 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    2. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Amine Masmoudi, M. & Coelho, Leandro C. & Demir, Emrah, 2022. "Plug-in hybrid electric refuse vehicle routing problem for waste collection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    4. Raeesi, Ramin & Zografos, Konstantinos G., 2020. "The electric vehicle routing problem with time windows and synchronised mobile battery swapping," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 101-129.
    5. Yang Wang & Alessandra Boggio-Marzet, 2018. "Evaluation of Eco-Driving Training for Fuel Efficiency and Emissions Reduction According to Road Type," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    6. Juan Francisco Coloma & Marta García & Gonzalo Fernández & Andrés Monzón, 2021. "Environmental Effects of Eco-Driving on Courier Delivery," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    7. Raeesi, Ramin & Zografos, Konstantinos G., 2022. "Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping," European Journal of Operational Research, Elsevier, vol. 301(1), pages 82-109.
    8. Brunner, Carlos & Giesen, Ricardo & Klapp, Mathias A. & Flórez-Calderón, Luz, 2021. "Vehicle routing problem with steep roads," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 1-17.
    9. Sadati, Mir Ehsan Hesam & Çatay, Bülent, 2021. "A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    10. Watling, David P. & Connors, Richard D. & Chen, Haibo, 2023. "Fuel-optimal truck path and speed profile in dynamic conditions: An exact algorithm," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1456-1472.
    11. Zhang, Shuai & Gajpal, Yuvraj & Appadoo, S.S. & Abdulkader, M.M.S., 2018. "Electric vehicle routing problem with recharging stations for minimizing energy consumption," International Journal of Production Economics, Elsevier, vol. 203(C), pages 404-413.
    12. Bektaş, Tolga & Ehmke, Jan Fabian & Psaraftis, Harilaos N. & Puchinger, Jakob, 2019. "The role of operational research in green freight transportation," European Journal of Operational Research, Elsevier, vol. 274(3), pages 807-823.
    13. Wang, Weiquan & Zhao, Jingyi, 2023. "Partial linear recharging strategy for the electric fleet size and mix vehicle routing problem with time windows and recharging stations," European Journal of Operational Research, Elsevier, vol. 308(2), pages 929-948.
    14. Bao, Dan-Wen & Zhou, Jia-Yi & Zhang, Zi-Qian & Chen, Zhuo & Kang, Di, 2023. "Mixed fleet scheduling method for airport ground service vehicles under the trend of electrification," Journal of Air Transport Management, Elsevier, vol. 108(C).
    15. Jaller, Miguel & Pahwa, Anmol & Zhang, Michael, 2021. "Cargo Routing and Disadvantaged Communities," Institute of Transportation Studies, Working Paper Series qt9qg2318x, Institute of Transportation Studies, UC Davis.
    16. Dönmez, Sercan & Koç, Çağrı & Altıparmak, Fulya, 2022. "The mixed fleet vehicle routing problem with partial recharging by multiple chargers: Mathematical model and adaptive large neighborhood search," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    17. Malladi, Satya S. & Christensen, Jonas M. & Ramírez, David & Larsen, Allan & Pacino, Dario, 2022. "Stochastic fleet mix optimization: Evaluating electromobility in urban logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    18. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    19. Cortés-Murcia, David L. & Prodhon, Caroline & Murat Afsar, H., 2019. "The electric vehicle routing problem with time windows, partial recharges and satellite customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 184-206.
    20. Wojciech Adamski & Krzysztof Brzozowski & Jacek Nowakowski & Tomasz Praszkiewicz & Tomasz Knefel, 2021. "Excess Fuel Consumption Due to Selection of a Lower Than Optimal Gear—Case Study Based on Data Obtained in Real Traffic Conditions," Energies, MDPI, vol. 14(23), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223006710. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.